Estimating the Probability of Default for No-Default and Low-Default Portfolios
Posted: 5 Nov 2018 Last revised: 2 Dec 2019
Date Written: May 20, 2019
This article proposes a sequential Bayesian updating approach to estimate default probabilities on rating grade level for no- and low-default portfolios. Bayesian sequential updating allows to obtain default probabilities also for those rating grades for which no defaults have been observed. The advantage of the proposed approach is that it preserves the rank order of rating grades in case of no defaults. Rank-preservation is not ensured when using an identical prior distribution across all rating grades. We discuss Bayesian sequential updating for the beta-binomial model and a model incorporating the asymptotic single risk factor model of the Basel Accord. Practical aspects such as incorporating information from external sources and the margin of conservatism are addressed.
Keywords: No-Default Portfolio, Low-Default Portfolio, Credit Rating, Probability of Default, Basel Accord, IFRS 9, CECL
JEL Classification: G21, G24, G28
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